Walking Fingerprinting Using Wrist Accelerometry during Activities of Daily Living in NHANES

Lily Koffman

Department of Biostatistics, Johns Hopkins School of Public Health

Introduction: accelerometry data

Introduction: accelerometry data

Introduction: big accelerometry data

Can we identify someone from their walking pattern measured by a wrist-worn accelerometer?

Problem setup

Problem setup

Problem setup

Big picture method: time series to scalar predictors

Fingerprints summarize predictors for a given lag and are different across individuals

Fingerprints summarize predictors for a given lag and are different across individuals

Model fitting

Results in labeled datasets

32 individuals, 6 minutes of walking each

100% rank-1 accuracy (Koffman et al. 2023)

153 individuals, 3 minutes of walking each

Two sessions, at least 1 week apart

Rank-1 (rank-5) % accuracies

  • Train and test on session 1
    • Logistic regression: 92 (97)
    • XGBoost: 93 (99)
  • Train on session 1, test on session 2
    • Logistic regression: 41 (75)
    • XGBoost: 58 (78)

(Koffman, Crainiceanu, and Leroux 2024)

Can we identify someone from their walking pattern measured by a wrist-worn accelerometer in large, unlabeled data sets?

NHANES data: recap

  • \(>15,000\) participants
  • \(7\) days of wrist accelerometry
  • \(10\)Tb of data
  • Open source processing pipeline
  • Publicly available data repository
  • First nationally representative estimate of steps in the US population using open source algorithms
Koffman and Muschelli (2025)

Walking fingerprinting in NHANES

Process outline

  • Use algorithm to identify walking
  • Partition data into train/test
    • Randomly sample training/testing seconds
    • Train on one day, test on later day
  • Fit models

Use algorithms to identify walking


ADaptive Empirical Pattern Transformation (ADEPT) (Karas et al. 2019)

library(adept)
adept::segmentWalking(
  xyz, # data frame of tri-axial accelerometry (3 cols)
  xyz.fs = 100, # sample rate 
  template = templates # list of templates for pattern matching
)



stepcount (Small et al. 2024)

devtools::install_github("jhuwit/stepcount")
library(stepcount)
stepcount::stepcount(file = sample_data, model_type = "ssl")

Use algorithms to identify walking

Use algorithms to identify walking

Use algorithms to identify walking

Partition data into train/test

Fit models

Questions

  • How important is sample size?
    • Fit on subgroups of size \(n=100\) and entire \(N\)
  • How important is walking identification algorithm accuracy?
    • Compare ADEPT, stepcount on same individuals
  • How important is length of training data?
    • Train on 3 min vs. 30 min.
  • How important is train/test partition type?
    • Compare random vs. temporal
  • How important is model choice?
    • Compare logistic regression, XGBoost, random forest
  • Can we improve logistic regression?
    • Weighting, oversampling

Sample size

Algorithm

Length of data

Train/test partition

Model choice

Model improvements

Dataset Model Rank 1 Rank 1% Rank 5 Rank 5%
Random
(n=13,367) 		Logistic 9.7 68 21 93
Oversampled at 10% 41 68 95 99
Weighted 34 96 61 100
Two-stage 20 68 37 93
Temporal
(n=10,770) 		Logistic 0.028 26 5.1 49
Oversampled at 10% 4.3 32 10 52
Weighted 1.8 23 5.1 45
Two-stage 5.2 26 10 49
Rank 1, rank 1%, rank 5, rank 5% accuracies of different model types on the entire population for each model. The best model in each category is bolded.

Fingerprints

Fingerprints

Future directions

Thank you!



References

Karas, Marta, Marcin Stra̧czkiewicz, William Fadel, Jaroslaw Harezlak, Ciprian M Crainiceanu, and Jacek K Urbanek. 2019. “Adaptive Empirical Pattern Transformation (ADEPT) with Application to Walking Stride Segmentation.” Biostatistics 22 (2): 331–47. https://doi.org/10.1093/biostatistics/kxz033.
Koffman, Lily, Ciprian Crainiceanu, and Andrew Leroux. 2024. “Walking Fingerprinting.” Journal of the Royal Statistical Society Series C: Applied Statistics 73 (5): 1221–41. https://doi.org/10.1093/jrsssc/qlae033.
Koffman, Lily, and John Muschelli. 2025. “Minute level step counts and physical activity data from the National Health and Nutrition Examination Survey (NHANES) 2011-2014.” PhysioNet. https://doi.org/10.13026/9N0R-TV02.
Koffman, Lily, Yan Zhang, Jaroslaw Harezlak, Ciprian Crainiceanu, and Andrew Leroux. 2023. “Fingerprinting Walking Using Wrist-Worn Accelerometers.” Gait & Posture 103 (June): 92–98. https://doi.org/10.1016/j.gaitpost.2023.05.001.
Small, Scott R, Shing Chan, Rosemary Walmsley, Lennart von Fritsch, Aidan Acquah, Gert Mertes, Benjamin G Feakins, et al. 2024. “Self-Supervised Machine Learning to Characterize Step Counts from Wrist-Worn Accelerometers in the UK Biobank.” Medicine and Science in Sports and Exercise 56 (10): 1945.

tinyurl.com/koff-enar25